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Estimates of the incidence of the acute respiratory distress syndrome (ARDS) in high- and middle-income countries vary from 10.1 to 86.2 per 100,000 person-years in the general population. The epidemiology of ARDS has not been reported for a low-income country at the level of the population, hospital, or intensive care unit (ICU). The Berlin definition may not allow identification of ARDS in resource-constrained settings. To estimate the incidence and outcomes of ARDS at a Rwandan referral hospital using the Kigali modification of the Berlin definition: without requirement for positive end-expiratory pressure, hypoxia cutoff of SpO2/FiO2 less than or equal to 315, and bilateral opacities on lung ultrasound or chest radiograph. We screened every adult patient for hypoxia at a public referral hospital in Rwanda for 6 weeks. For every patient with hypoxia, we collected data on demographics and ARDS risk factors, performed lung ultrasonography, and evaluated chest radiography when available. Forty-two (4.0%) of 1,046 hospital admissions met criteria for ARDS. Using various prespecified cutoffs for the SpO2/FiO2 ratio resulted in almost identical hospital incidence values. Median age for patients with ARDS was 37 years, and infection was the most common risk factor (44.1%). Only 30.9% of patients with ARDS were admitted to an ICU, and hospital mortality was 50.0%. Using traditional Berlin criteria, no patients would have met criteria for ARDS. ARDS seems to be a common and fatal syndrome in a hospital in Rwanda, with few patients admitted to an ICU. The Berlin definition is likely to underestimate the impact of ARDS in low-income countries, where resources to meet the definition requirements are lacking. Although the Kigali modification requires validation before widespread use, we hope this study stimulates further work in refining an ARDS definition that can be consistently used in all settings.

Identifying new effective treatments for the acute respiratory distress syndrome (ARDS), including COVID-19 ARDS, remains a challenge. The field of ARDS investigation is moving increasingly toward innovative approaches such as the personalization of therapy to biological and clinical sub-phenotypes. Additionally, there is growing recognition of the importance of the global context to identify effective ARDS treatments. This review highlights emerging opportunities and continued challenges for personalizing therapy for ARDS, from identifying treatable traits to innovative clinical trial design and recognition of patient-level factors as the field of critical care investigation moves forward into the twenty-first century.

Caring for critically ill patients is challenging in resource-limited settings, where the burden of disease and mortality from potentially treatable illnesses is higher than in resource-rich areas. Barriers to delivering quality critical care in these settings include lack of epidemiologic data and context-specific evidence for medical decision-making, deficiencies in health systems organization and resources, and institutional obstacles to implementation of life-saving interventions. Potential solutions include the development of common definitions for intensive care unit (ICU), intensivist, and intensive care to create a universal ICU organization framework; development of educational programs for capacity building of health care professionals working in resource-limited settings; global prioritization of epidemiologic and clinical research in resource-limited settings to conduct timely and ethical studies in response to emerging threats; adaptation of international guidelines to promote implementation of evidence-based care; and strengthening of health systems that integrates these interventions. This manuscript reviews the field of global critical care, barriers to safe high-quality care, and potential solutions to existing challenges. We also suggest a roadmap for improving the treatment of critically ill patients in resource-limited settings.

To study the epidemiology and outcomes of delirium among hospitalized patients in Zambia. We conducted a prospective cohort study at the University Teaching Hospital in Lusaka, Zambia, from October 2017 to April 2018. The primary exposure was delirium duration over the initial 3 days of hospitalization, assessed daily using the Brief Confusion Assessment Method. The primary outcome was 6-month mortality. Secondary outcomes included 6-month disability, evaluated using the World Health Organization Disability Assessment Schedule 2.0. 711 adults were included (median age, 39 years; 461 men; 459 medical, 252 surgical; 323 with HIV). Delirium prevalence was 48.5% (95% CI, 44.8%-52.3%). 6-month mortality was higher for delirious participants (44.6% [39.3%-50.1%]) versus non-delirious participants (20.0% [15.4%-25.2%]; P < .001). After adjusting for covariates, delirium duration independently predicted 6-month mortality and disability with a significant dose-response association between number of days with delirium and odds of worse clinical outcome. Compared to no delirium, presence of 1, 2 or 3 days of delirium resulted in odds ratios for 6-month mortality of 1.43 (95% CI, 0.73-2.80), 2.20 (1.07-4.51), and 3.92 (2.24-6.87), respectively (P < .001). Odds of 6-month disability were 1.20 (0.70-2.05), 1.73 (0.95-3.17), and 2.80 (1.78-4.43), respectively (P < .001). Among hospitalized medical and surgical patients in Zambia, delirium prevalence was high and delirium duration independently predicted mortality and disability at 6 months. This work lays the foundation for prevention, detection, and management of delirium in low-income countries. Long-term follow up of outcomes of critical illness in resource-limited settings appears feasible using the WHO Disability Assessment Schedule.

Resistance among bacterial infections is increasingly well-documented in high-income countries; however, relatively little is known about bacterial antimicrobial resistance in low-income countries, where the burden of infections is high. We prospectively screened all adult inpatients at a referral hospital in Rwanda for suspected infection for seven months. Blood, urine, wound and sputum samples were cultured and tested for antibiotic susceptibility. We examined factors associated with resistance and compared hospital outcomes for participants with and without resistant isolates. We screened 19,178 patient-days, and enrolled 647 unique participants with suspected infection. We obtained 942 culture specimens, of which 357 were culture-positive specimens. Of these positive specimens, 155 (43.4%) were wound, 83 (23.2%) urine, 64 (17.9%) blood, and 55 (15.4%) sputum. Gram-negative bacteria comprised 323 (88.7%) of all isolates. Of 241 Gram-negative isolates tested for ceftriaxone, 183 (75.9%) were resistant. Of 92 Gram-negative isolates tested for the extended spectrum beta-lactamase (ESBL) positive phenotype, 66 (71.7%) were ESBL positive phenotype. Transfer from another facility, recent surgery or antibiotic exposure, and hospital-acquired infection were each associated with resistance. Mortality was 19.6% for all enrolled participants. This is the first published prospective hospital-wide antibiogram of multiple specimen types from East Africa with ESBL testing. Our study suggests that low-resource settings with limited and inconsistent access to the full range of antibiotic classes may bear the highest burden of resistant infections. Hospital-acquired infections and recent antibiotic exposure are associated with a high proportion of resistant infections. Efforts to slow the development of resistance and supply effective antibiotics are urgently needed.

Intensive Care Unit (ICU) risk prediction models are used to compare outcomes for quality improvement initiatives, benchmarking, and research. While such models provide robust tools in high-income countries, an ICU risk prediction model has not been validated in a low-income country where ICU population characteristics are different from those in high-income countries, and where laboratory-based patient data are often unavailable. We sought to validate the Mortality Probability Admission Model, version III (MPM0-III) in two public ICUs in Rwanda and to develop a new Rwanda Mortality Probability Model (R-MPM) for use in low-income countries. We prospectively collected data on all adult patients admitted to Rwanda's two public ICUs between August 19, 2013 and October 6, 2014. We described demographic and presenting characteristics and outcomes. We assessed the discrimination and calibration of the MPM0-III model. Using stepwise selection, we developed a new logistic model for risk prediction, the R-MPM, and used bootstrapping techniques to test for optimism in the model. Among 427 consecutive adults, the median age was 34 (IQR 25-47) years and mortality was 48.7%. Mechanical ventilation was initiated for 85.3%, and 41.9% received vasopressors. The MPM0-III predicted mortality with area under the receiver operating characteristic curve of 0.72 and Hosmer-Lemeshow chi-square statistic p = 0.024. We developed a new model using five variables: age, suspected or confirmed infection within 24 hours of ICU admission, hypotension or shock as a reason for ICU admission, Glasgow Coma Scale score at ICU admission, and heart rate at ICU admission. Using these five variables, the R-MPM predicted outcomes with area under the ROC curve of 0.81 with 95% confidence interval of (0.77, 0.86), and Hosmer-Lemeshow chi-square statistic p = 0.154. The MPM0-III has modest ability to predict mortality in a population of Rwandan ICU patients. The R-MPM is an alternative risk prediction model with fewer variables and better predictive power. If validated in other critically ill patients in a broad range of settings, the model has the potential to improve the reliability of comparisons used for critical care research and quality improvement initiatives in low-income countries.

Few studies have assessed the presentation, management, and outcomes of sepsis in low-income countries (LICs). We sought to characterize these aspects of sepsis and to assess mortality predictors in sepsis in two referral hospitals in Rwanda. This was a retrospective cohort study in two public academic referral hospitals in Rwanda. Data was abstracted from paper medical records of adult patients who met our criteria for sepsis. Of the 181 subjects who met eligibility criteria, 111 (61.3%) met our criteria for sepsis without shock and 70 (38.7%) met our criteria for septic shock. Thirty-five subjects (19.3%) were known to be HIV positive. The vast majority of septic patients (92.7%) received intravenous fluid therapy (median = 1.0 L within 8 hours), and 94.0% received antimicrobials. Vasopressors were administered to 32.0% of the cohort and 46.4% received mechanical ventilation. In-hospital mortality for all patients with sepsis was 51.4%, and it was 82.9% for those with septic shock. Baseline characteristic mortality predictors were respiratory rate, Glasgow Coma Scale score, and known HIV seropositivity. Septic patients in two public tertiary referral hospitals in Rwanda are young (median age = 40, IQR = 29, 59) and experience high rates of mortality. Predictors of mortality included baseline clinical characteristics and HIV seropositivity status. The majority of subjects were treated with intravenous fluids and antimicrobials. Further work is needed to understand clinical and management factors that may help improve mortality in septic patients in LICs.

To identify risk factors for delirium among hospitalized patients in Zambia. We conducted a prospective cohort study at the University Teaching Hospital in Lusaka, Zambia, from October 2017 to April 2018. We report associations of exposures including sociodemographic and clinical factors with delirium over the first three days of hospital admission, assessed using a modified Brief Confusion Assessment Method (bCAM). 749 patients were included for analysis (mean age, 42.9 years; 64.8% men; 47.3% with HIV). In individual regression analyses of potential delirium risk factors adjusted for age, sex and education, factors significantly associated with delirium included being divorced/widowed (OR 1.64, 95% CI 1.09-2.47), lowest tercile income (OR 1.58, 95% CI 1.04-2.40), informal employment (OR 1.97, 95% CI 1.25-3.15), untreated HIV infection (OR 2.18, 95% CI 1.21-4.06), unknown HIV status (OR 2.90, 95% CI 1.47-6.16), history of stroke (OR 2.70, 95% CI 1.15-7.19), depression/anxiety (OR 1.52, 95% CI 1.08-2.14), alcohol overuse (OR 1.96, 95% CI 1.39-2.79), sedatives ordered on admission (OR 3.77, 95% CI 1.70-9.54), severity of illness (OR 2.00, 95% CI 1.82-2.22), neurological (OR 7.66, 95% CI 4.90-12.24) and pulmonary-system admission diagnoses (OR 1.91, 95% CI 1.29-2.85), and sepsis (OR 2.44, 95% CI 1.51-4.08). After combining significant risk factors into a multivariable regression analysis, severity of illness, history of stroke, and being divorced/widowed remained predictive of delirium (p<0.05). Among hospitalized adults at a national referral hospital in Zambia, severity of illness, history of stroke, and being divorced/widowed were independently predictive of delirium. Extension of this work will inform future efforts to prevent, detect, and manage delirium in low- and middle-income countries.

Mortality prediction among critically ill patients in resource limited settings is difficult. Identifying the best mortality prediction tool is important for counseling patients and families, benchmarking quality improvement efforts, and defining severity of illness for clinical research studies. Compare predictive capacity of the Modified Early Warning Score (MEWS), Universal Vital Assessment (UVA), Tropical Intensive Care Score (TropICS), Rwanda Mortality Probability Model (R-MPM), and quick Sequential Organ Failure Assessment (qSOFA) for hospital mortality among adults admitted to a medical-surgical intensive care unit (ICU) in rural Kenya. We performed a pre-planned subgroup analysis among ICU patients with suspected infection. Prospective single-center cohort study at a tertiary care, academic hospital in Kenya. All adults 18 years and older admitted to the ICU January 2018-June 2019 were included. The primary outcome was association of clinical prediction tool score with hospital mortality, as defined by area under the receiver operating characteristic curve (AUROC). Demographic, physiologic, laboratory, therapeutic, and mortality data were collected. 338 patients were included, none were excluded. Median age was 42 years (IQR 33-62) and 61% ( = 207) were male. Fifty-nine percent ( = 199) required mechanical ventilation and 35% ( = 118) received vasopressors upon ICU admission. Overall hospital mortality was 31% ( = 104). 323 patients had all component variables recorded for R-MPM, 261 for MEWS, and 253 for UVA. The AUROC was highest for MEWS (0.76), followed by R-MPM (0.75), qSOFA (0.70), and UVA (0.69) ( < 0.001). Predictive capacity was similar among patients with suspected infection. All tools had acceptable predictive capacity for hospital mortality, with variable observed availability of the component data. R-MPM and MEWS had high rates of variable availability as well as good AUROC, suggesting these tools may prove useful in low resource ICUs.